The paper, written by Marco Viceconti of the Department of Industrial Engineering at the University of Bologna, introduces what the author calls the 7S Framework, a seven-step general procedure for evaluating the credibility of any quantitative estimate, whether it originates from a sensor, a statistical model, or a predictive simulation. The motivation is practical rather than purely philosophical. In fields such as in silico medicine, where computational models increasingly inform clinical decisions and regulatory submissions, a new generation of tools refuses to sit neatly within any one of the traditional categories. In silico-augmented clinical trials, physics-informed machine learning predictors, and machine learning models trained on synthetic datasets all blend measured data, statistical inference, and causal prediction into single estimators, leaving established credibility frameworks unable to cover them cleanly.

To build the framework, Viceconti begins with a conceptual scaffolding often described as the pyramid of knowledge. In this picture, observation lifts raw signals produced by a system of interest into data; annotation with metadata about who, what, where, and when lifts data into information; modelling correlations lifts information into tentative causal beliefs; and subjecting those beliefs to falsification experiments lifts them into actionable knowledge. Quantitative information, in this scheme, is an annotated set of values whose metadata specifies the domain of information, everything sender and receiver must know for the values to be meaningful, but without the causal “why” that would elevate it to knowledge. New information can be created by measurement, which converts signals into data; by inference, which derives new information from existing information; or by prediction, which uses causal knowledge to generate estimates of quantities that were never observed.

From these foundations, the author generalises a vocabulary that statistics normally reserves for inference. The target quantity is the estimand, the value produced is the estimate, and whatever produces it, be it a thermometer, a regression, or a finite element model, is an estimator. Credibility is then defined with a deliberately demanding definition: the minimum accuracy with which an estimator recovers the true value of the estimand across the entire information space, the bounded multidimensional region defined by all the observable quantities on which the quantity of interest depends. Accuracy itself borrows from metrology, where trueness captures systematic error and precision captures random error, combined into a normalised class of accuracy averaged over repeated estimations.

A crucial insight of the paper is that credibility expectations come in three levels, and that these levels are properties of the intended use rather than of the type of estimator. Level 1 credibility demands only that an estimate fall within a predefined uncertainty band around the true value, appropriate when knowing the order of magnitude suffices. Level 2 requires that the average of repeated estimates match the true value in a statistical sense, as when comparing central properties of populations. Level 3 demands local accuracy at every validation point, the standard for subject-specific models intended to predict individual outcomes. A predictive model can therefore be L1, L2, or L3 credible depending on whether it is meant to capture a scale, a population mean, or a person-specific value, and the same hierarchy applies to measurement and inference.

Because brute-force induction, measuring the error at an effectively infinite number of points, is practically impossible and not even theoretically guaranteed for all estimators, the framework follows the strategy shared by metrology, statistics, and VVUQ: decompose the estimation error into its sources and check that each component behaves as theory predicts for a well-behaved estimator. The seven steps formalise this logic. Step S1 defines the context of use and the acceptable error threshold, the maximum error that still leaves the information useful for the decision it must support, and sets this against the limits of validity imposed by the physics of the phenomenon. Step S2 establishes the source of true values, insisting on measurement chains at least an order of magnitude more accurate than the threshold. Step S3 quantifies estimation error through controlled experiments sampled across the solution space. Step S4 identifies the sources of error, which the paper groups into approximation, aleatoric, and epistemic contributions. Step S5 decomposes the overall error among these sources, sometimes requiring special experiments in which all but one error source is excluded. Step S6 critically reviews whether each error component is distributed as expected. Step S7 examines robustness to biases that could emerge in routine use, including applicability, the guarantee that real-world inputs never stray beyond the validity limits explored during assessment. Transparency throughout, particularly about which error sources are considered and how they are separated, is flagged as essential.

The paper demonstrates the framework on seven use cases drawn from the author’s research programme, three of which are summarised in detail. The first concerns strain gauge measurements of bone tissue deformation, used to validate finite element models that predict fracture. The context of use fixes an error threshold derived from the strain difference used to determine fracture, attenuated by two orders of magnitude to account for the chain of inference. True values come from beam-theory calculations on machined aluminium alloy specimens corrected for curvature error; trueness is computed as a root-mean-square error; normality tests confirm the expected distribution of random and systematic errors; and repeated tests on bone specimens establish applicability.

The second case applies the framework to the BBCT-Hip predictor, a biophysical model that estimates mechanical strains in a patient’s bone from a calibrated computed tomography scan. Here the error threshold is set at two percent of the cortical bone failure strain in compression, about 146 microstrain. True values come from strain gauge measurements on carefully preserved cadaveric femurs. The model’s predictions carry numerical, aleatoric, and epistemic errors, and the VVUQ procedure separates them, with the numerical component required to be negligible, the aleatoric component normally distributed with a near-zero mean, and the epistemic component showing a root-mean-square error close to zero. Applicability is probed by exhaustive experiments spanning inter-subject variability and all relevant loading conditions.

The third case is the most forward-looking: assessing a synthetic cohort inferred from a real clinical cohort of elderly patients at risk of hip fracture. The goal is to run in silico trials on virtual populations far larger than any experimentally collected cohort could be, comparing central properties such as means and medians of feature distributions and model predictions. The error thresholds are tied to the measurement and prediction accuracy of each quantity; epistemic error vanishes because the synthetic data are generated by inference, leaving aleatoric error from measurement uncertainty and numerical error from the interpolation functions, which must be shown negligible by sensitivity analysis. Applicability restricts use of the synthetic cohort to the portion of the information space actually sampled by the clinical data.

Viceconi is careful about scope. For estimators that fall squarely into the classical categories, he recommends continuing to use metrology, statistics, or VVUQ, which are more mature and widely accepted within their communities. The 7S Framework is positioned as a supplement for the growing class of hybrid estimators that do not fit anywhere: in silico-augmented trials that inject model predictions into Bayesian device trials, physics-informed neural networks that encode biomechanical law inside learned predictors, synthetic datasets generated to sidestep privacy constraints, and machine learning surrogates trained to replace expensive biophysical simulations. The framework was also applied to cases covering fracture-risk prediction and machine learning surrogates of computational models, and the author reports that it proved effective, sufficiently general, and sensitive to the subtle differences in what credibility means for each information type. Limitations acknowledged include the restriction of the exposition to single scalar quantities, although extension to vectors and time-dependent quantities requires only adding a norm, and the framework’s status as a generalisation rather than a replacement of existing practice. As computational medicine pushes further into regulatory territory, the stakes of getting credibility assessment right rise accordingly, and a shared epistemological vocabulary spanning measurement, inference, and prediction may prove to be exactly what regulators, developers, and clinicians need.

Subject of Research: A general seven-step framework for assessing the credibility of measured, inferred, and predicted quantitative information in computational medicine

Article Title: Assessing the Credibility of Quantitative Information: A General Framework

Article References: Viceconti, M. (2026). Assessing the Credibility of Quantitative Information: A General Framework. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-026-04367-4

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04367-4

Keywords: credibility assessment, quantitative information, metrology, statistical inference, verification validation and uncertainty quantification, in silico medicine, machine learning predictors, synthetic data, 7S Framework, biomedical engineering, clinical decision-making, predictive models